Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

If one connects loudspeakers to an electrode implanted in brain cells, one would hear a rough, crackling and apparently unstructured sound. Is this noise a reflection of intrinsic imprecisions of an organic form of information processing or, rather, is it the result of an unknown and perhaps highly optimized way of encoding information? At the center of neuroscience research lies this problem of neural coding. It has become clear that peripheral nerves code information streaming from the senses in their spiking rate. Neurons within the hierarchical structure of the neocortex, however, constantly combine information of two different natures: bottom-up information coming more directly from the senses and top-down information coming from internal sources. Therefore, we propose a simple reformulation of the neural coding problem and ask the general question: How can a single population of neurons encode two streams of information simultaneously? Recent experimental evidence point to a pivotal role of dendrites in answering this question.

Using numerical simulations of neocortical networks, this grant will (1) determine the role of dendrite-dependent bursting for representing top-down and bottom-up information simultaneously. In addition, the simulations will be used to (2) investigate the role of inhibitory connection motifs to optimize the bursting neural code. Lastly, we will (3) develop statistical data analysis methods to facilitate experimental investigations of dendrite-dependent burst coding.

The most powerful machine learning method of today, deep learning, was inspired by the hierarchical structure of the neocortex. By outlining the rules for neural coding in a hierarchy, the proposed work can inspire efficient implementations of signal processing algorithms. In addition, understanding the neural code used by the neocortex is essential to the analysis of biomedical data. To single out a possible area of application, we note that the improvement of brain-machine interface technology strongly depends on novel decoding algorithms of the type discussed in this proposal. Therefore, our novel approach to the problem of neural coding can lead to valuable technologies.